Patient-derived models of acquired resistance can identify effective drug combinations for cancer

Title
Patient-derived models of acquired resistance can identify effective drug combinations for cancer
Authors
Adam S. CrystalAlice T. ShawLecia V. SequistLuc FribouletMatthew J. NiederstElizabeth L. LockermanRosa L. FriasJustin F. GainorArnaud AmzallagPatricia GreningerDana LeeAnuj KalsyMaria Gomez-CaraballoLeila ElamineEmily Howe허우영Eugene LifshitsHayley E. RobinsonRyohei KatayamaAnthony C. FaberMark M. AwadSridhar RamaswamyMari Mino-KenudsonA. John IafrateCyril H. BenesJeffrey A. Engelman
Issue Date
2014-12
Publisher
Science
Citation
VOL 346, NO 6216, 1480-1486
Abstract
Targeted cancer therapies have produced substantial clinical responses, but most tumors develop resistance to these drugs. Here, we describe a pharmacogenomic platform that facilitates rapid discovery of drug combinations that can overcome resistance.We established cell culture models derived from biopsy samples of lung cancer patients whose disease had progressed while on treatment with epidermal growth factor receptor (EGFR) or anaplastic lymphoma kinase (ALK) tyrosine kinase inhibitors and then subjected these cells to genetic analyses and a pharmacological screen. Multiple effective drug combinations were identified. For example, the combination of ALK and MAPK kinase (MEK) inhibitors was active in an ALK-positive resistant tumor that had developed a MAP2K1 activating mutation, and the combination of EGFR and fibroblast growth factor receptor (FGFR) inhibitors was active in an EGFR mutant resistant cancer with a mutation in FGFR3. Combined ALK and SRC (pp60c-src) inhibition was effective in several ALK-driven patient-derived models, a result not predicted by genetic analysis alone.With further refinements, this strategy could help direct therapeutic choices for individual patients.
URI
http://pubs.kist.re.kr/handle/201004/51074
ISSN
00368075
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KIST Publication > Article
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